#coding:utf-8

import  tensorflow as tf
import  numpy as np
import matplotlib.pyplot as plt
from IPython.core import display_trap
from IPython.core.display import display

rge=np.random

#Hyper Paramters

learn_rate=0.01
training_epochs=1000
display_step=50

#Training Data
train_X = np.asarray([3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779, 6.182, 7.59, 2.167,
                         7.042, 10.791, 5.313, 7.997, 5.654, 9.27, 3.1])
train_Y = np.asarray([1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366, 2.596, 2.53, 1.221,
                         2.827, 3.465, 1.65, 2.904, 2.42, 2.94, 1.3])
n_samples = train_X.shape[0]

#tf Graph Input
X=tf.placeholder(dtype='float')
Y=tf.placeholder(dtype='float')

#init model weights
W=tf.Variable(np.random.rand(),name='weight')
b=tf.Variable(np.random.rand(),name='bias')

#Construct a linear error
pred=tf.add(tf.multiply(X,W),b)

#Mean squared error:loss function
cost=tf.reduce_sum(tf.pow(pred-Y,2))/(2*n_samples)

#Gradient descent
optimizer=tf.train.GradientDescentOptimizer(learning_rate=learn_rate).minimize(cost)

#Initialize the variables
init=tf.global_variables_initializer()

#Start training
with tf.Session() as sess:
    sess.run(init)#执行初始化

    #fit all training data
    for epoch in range(training_epochs):
        for (x,y) in zip(train_X,train_Y):
            sess.run(optimizer,feed_dict={X:x,Y:y})
        #display logs for per epoch step
        if (epoch+1) % display_step ==0:
            c=sess.run(cost,feed_dict={X:train_X,Y:train_Y})
            print('Epoch:','%04d'%(epoch+1),'cost=','{:.9f}'.format(c),'W=',sess.run(W),'b=',sess.run(b))

    print("Optimization Finished!")
    training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})
    print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n')

    #Graphic display
    plt.plot(train_X,train_Y,'ro',label='Original data')
    plt.plot(train_X,sess.run(W)*train_X+sess.run(b),label='fit line')
    plt.legend()#显示图例
    plt.show()
